Integrating learning data provided by an external learning platform to create a custom learner experience within the context of an application provided by a cloud computing platform

ABSTRACT

Technologies are provided for integrating learning data provided by an external learning platform (ELP) to create a custom learner experience within a context of an application provided by a cloud computing platform (CCP). The system can include the CCP, the ELP, learner APIs that expose a common learning data schema on the CCP, and a user interface platform (UIP). The UIP can include a compiler that transforms source code of UICs of a componentized learner user interface for usage on the CCP, and a bundler that generates a package of UI components (UICs) that are compatible for usage on the CCP. The UICs are specific to the learning data schema shared with the learner APIs. The UIP exports the package to the CCP, which composes the learning data provided via learner APIs and UICs from the package to provide the custom learner experience within the context of the application.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional Application No. 63/080,608, filed Sep. 18, 2020, which is incorporated herein by reference in its entirety. The present application is related to co-pending application Ser. No. ______, titled “PROVISIONING AN ESCROW USER ACCOUNT FOR TRACKING LEARNING PROGRESS OF AN END USER OF A CLOUD COMPUTING PLATFORM WHILE INTERACTING WITH VIRTUAL LEARNING ENTITIES OF THE CLOUD COMPUTING PLATFORM THAT REPRESENT CONTENT OF AN EXTERNAL LEARNING APPLICATION,” also filed on Sep. 17, 2021, by inventors John Bracken et al., which is incorporated herein by reference in its entirety.

TECHNICAL FIELD

Embodiments of the subject matter described herein relate generally to cloud computing platforms, and more particularly, embodiments of the subject matter relate to methods, systems and non-transient processor-readable media that are provided for integrating learning data provided by an external learning platform to create a custom learner experience within the context of an application provided by a cloud computing platform.

BACKGROUND

Today many enterprises now use cloud-based computing platforms that allow services and data to be accessed over the Internet (or via other networks). Infrastructure providers of these cloud-based computing platforms offer network-based processing systems that often support multiple enterprises (or tenants) using common computer hardware and data storage. “Cloud computing” services provide shared resources, software, and information to computers and other devices upon request. In cloud computing environments, software can be accessible over the Internet rather than installed locally on in-house computer systems. This “cloud” computing model allows applications to be provided over a platform “as a service” supplied by the infrastructure provider. The infrastructure provider typically abstracts the underlying hardware and other resources used to deliver a customer-developed application so that the customer no longer needs to operate and support dedicated server hardware. Cloud computing typically involves over-the-Internet provision of dynamically scalable and often virtualized resources. Technological details can be abstracted from the users, who no longer have need for expertise in, or control over, the technology infrastructure “in the cloud” that supports them. The cloud computing model can often provide substantial cost savings to the customer over the life of the application because the customer no longer needs to provide dedicated network infrastructure, electrical and temperature controls, physical security and other logistics in support of dedicated server hardware.

Multi-tenant cloud-based architectures have been developed to improve collaboration, integration, and community-based cooperation between customer tenants without compromising data security. Generally speaking, multi-tenancy refers to a system where a single hardware and software platform simultaneously supports multiple organizations or tenants from a common data storage element (also referred to as a “multi-tenant database”). The multi-tenant design provides a number of advantages over conventional server virtualization systems. First, the multi-tenant platform operator can often make improvements to the platform based upon collective information from the entire tenant community. Additionally, because all users in the multi-tenant environment execute applications within a common processing space, it is relatively easy to grant or deny access to specific sets of data for any user within the multi-tenant platform, thereby improving collaboration and integration between applications and the data managed by the various applications. The multi-tenant architecture therefore allows convenient and cost-effective sharing of similar application feature software between multiple sets of users.

In general, businesses use a customer relationship management (CRM) system (also referred to as a database system or system) to manage business relationships and information associated with the business relationship. For example, a multi-tenant system may support an on-demand CRM application that manages the data for a particular organization's sales staff that is maintained by the multi-tenant system and facilitates collaboration among members of that organization's sales staff (e.g., account executives, sales representatives, and the like). This data may include customer and prospect contact information, accounts, leads, and opportunities in one central location. The information may be stored in a database as objects. For example, the CRM system may include “account” object, “contact” object and “opportunities” object.

Learning to use applications and services provided by a cloud computing platform can be time consuming for end users. In one approach to help facilitate learning within a cloud computing platform, an external learning system may provide data to the cloud computing platform, and a platform developer can build their own UI (e.g., representation of the data) without importing the application functionality and style of the application at the external learning platform. This can allow an end user or “learner” to learn within the context of an application provided by the cloud computing platform, but the end user will not have the same user experience as they would if interacting with a learning application provided by the external learning platform. In most case, the look and feel, application functionality, information architecture, interaction behavior, style and/or branding of the external learning application provided by the external learning platform will be different than if the end user were interacting with the learning application provided by the external learning platform.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the subject matter may be derived by referring to the detailed description and claims when considered in conjunction with the following figures, wherein like reference numbers refer to similar elements throughout the figures.

FIG. 1 is a schematic block diagram of an example of a computing environment in accordance with the disclosed embodiments.

FIG. 2 is a schematic block diagram of another example of a cloud computing environment for implementing a learning platform architecture (LPA) in accordance with the disclosed embodiments.

FIG. 3 is a flowchart of a method in accordance with the disclosed embodiments.

FIG. 4 is a set of screenshots that illustrates various user interfaces that are presented to an end user during a learner experience when interacting with the learning platform architecture (LPA) of FIG. 2.

FIG. 5 is a screenshot of a user interface that shows the user interface elements of FIG. 4 in greater resolution.

FIG. 6 is a schematic block diagram of an example of a multi-tenant computing environment in which features of the disclosed embodiments can be implemented in accordance with the disclosed embodiments.

FIG. 7 shows a block diagram of an example of an environment in which an on-demand database service can be used in accordance with some implementations.

FIG. 8 shows a block diagram of example implementations of elements of FIG. 4 and example interconnections between these elements according to some implementations.

FIG. 9A shows a system diagram illustrating example architectural components of an on-demand database service environment according to some implementations.

FIG. 9B shows a system diagram further illustrating example architectural components of an on-demand database service environment according to some implementations.

FIG. 10 is a block diagram that illustrates a diagrammatic representation of a machine in the exemplary form of a computer system within which a set of instructions, for causing the machine to perform any one or more of the methodologies discussed herein, may be executed.

DETAILED DESCRIPTION

It would be desirable to provide a cloud computing platform with the ability to integrate and leverage third-party data, features, user interface design patterns, and branding from external third-party content platforms (e.g., data sources) into an application provided by the cloud computing platform. However, this can present a number of technical challenges. For example, in some core cloud computing platforms, such as the Salesforce.com® platform, when third-party data from an external third-party content platform or other data source is to be incorporated into an application provided by the cloud computing platform, it is imported via APIs, then reformatted to meet constraints of the cloud computing platform. At that point, developers on the cloud computing platform then have to create user interfaces (UIs) to represent that data from scratch. In addition, the resulting user experience is not entirely customizable. The UI components are opinionated and support a small number of modifications (by design) to ensure continuity throughout the integration. Additionally, the APIs for the components are strict, so there must be work done in the platform integration process to aggregate and serialize the data such that it is compatible with the UI components. As a result, this process is time-consuming and prone to error since it requires time and effort to reformat the third-party data and then build new UI(s) from scratch.

To help address the problems described above, technologies and methodologies are provided for packaging UI components that are designed to represent third-party data from an external third-party content platform (or data source). They retain the interactivity, formatting, and look/feel intended for the user interface by the third-party content platform. For example, the platform has some hierarchy of entities so they provide a UI component that encapsulates a design pattern for displaying a nested list of the entities and their relationships. This way the data for these entities can be displayed consistently between platforms where the third party is integrated (the UI would be the same in Salesforce®, Oracle®, SAP®, Shopify®, etc) with reasonable customization capabilities. This mechanism also allows to seamlessly carry over the branding associated with the UI from the external platform. Each UI package is compatible for usage on a particular application platform and specifically compiled for interoperability with that particular application platform. Each UI package can be used by a platform integrator of a particular target platform (e.g., a cloud computing platform such as the Salesforce.com® platform) to compose UI components from that package instead of building them from scratch. This way, the disclosed technologies and methodologies can allow the design “language” of the external third-party content platform (or data source) to be retained. The disclosed technologies and methodologies can eliminate the need to reformat the third-party data to meet constraints of the cloud computing platform, while also eliminating the need to create user interfaces (to represent that data) from scratch on the cloud computing platform. As such, the disclosed technologies and methodologies are less time-consuming, less prone to error and save considerable time and effort that would otherwise be required.

The disclosed embodiments can provide methods, systems and non-transient processor-readable media for integrating learning data (e.g., learning content and contextual user information) provided by an external learning platform to create a custom learner experience within the context of an application provided by a target application platform (e.g., a cloud computing platform). Examples of contextual user information can include any information about the user's relationship with the learning data including, but not limited to, learning lists which are user specified lists of learning content. An end user can have many learning lists. Each learning list can have many learning items. An end user has relationships with each learning list, such as, learning or “learner” progress, learning start date time that specifies the date and time started, learning completed date and time that specifies when the learning module was completed, learning time remaining to complete, learning reward information, learning submission attempt dates and times, learning submissions, modules bookmarked by an end user, modules favorited by an end user, etc.

In some embodiments, the system can include the cloud computing platform, the external learning platform, learner application programming interfaces (APIs) that expose a common learning data schema on the cloud computing platform, and a user interface platform. The learner APIs and the user interface components have an implicit shared knowledge of the common learning data schema that results in interoperability between them. The user interface platform can include a compiler and a bundler. The compiler is configured to transform (e.g., transpile) source code of user interface components of a componentized learner user interface for usage on the cloud computing platform. The user interface components are specific to the common learning data schema shared with the learner APIs. The bundler is configured to generate a package of user interface components that are compatible for usage on the cloud computing platform. The user interface platform is configured to export the package to the cloud computing platform. The cloud computing platform composes the learning data provided via the learner application programming interfaces and the user interface components from the package to provide a custom learner experience within the context of the application provided by the cloud computing platform. Depending on the implementation, the cloud computing platform may compose the learner application interfaces and the user interface components from the package via one or more of: application code, composition in an experience builder by an administrator, or an automation application.

Thus, the user interface platform can create packages for any number of application platforms to provide learning data and UI components from a single source such that a design and features are consistently represented across integrations. These packages be used to create “custom learner experiences” with the context of applications provided by those application platforms. The custom learner experiences will all retain the same (or similar) look and feel, application functionality, information architecture, interaction behavior (events), style and/or branding. For example, the user interface platform can facilitate the integration of external learning data (provided via the learner APIs) and UI components provided via a platform package to create a custom learner experience within the context of an application provided by the cloud computing platform. Similarly, the user interface platform can facilitate the integration of external learning data and UI components provided via another platform package to create another custom learner experience within the context of an external learning application provided by the external learning platform. Likewise, the user interface platform can also facilitate the integration of external learning data and UI components provided to create any number of other custom learner experiences within the context of any number of other applications provided by any number of other application platforms, while retaining same (or similar) look and feel, application functionality, information architecture, interaction behavior (events), style and/or branding across all of these application platforms including, but not limited to, the cloud computing platform and the external learning platform.

In some non-limiting embodiments, the cloud computing platform implements external learning entities that are instances of the learner APIs that make the common learning data schema available within the cloud computing platform. The external learning entities comprise the learning data, and have attributes expected by the user interface components.

In some non-limiting embodiments, the package of user interface components is specific to the cloud computing platform, and can include transformed source code, assets, configuration and metadata of the user interface components. In one embodiment, the package comprises source code specifically compiled for interoperability with the cloud computing platform. In one non-limiting implementation, the package can include, for example, metadata, configuration, JavaScript, stylesheets, images, and other artifacts of the packaging performed by the bundler.

In some non-limiting embodiments, the cloud computing platform comprises a platform compiler that is configured to: transform (e.g., transpile) the source code of the package into application code of the cloud computing platform.

In some non-limiting embodiments, the user interface components provide an eventing function for communication with the cloud computing platform that allows for integration with the application provided by the cloud computing platform.

In some non-limiting embodiments, the user interface components provide an eventing integration function for interacting with resources from the external learning platform system within an iFrame, wherein the eventing integration function allows for the external learning platform to communicate with the application provided by the cloud computing platform.

In one embodiment, a cloud-based computing system is provided. The cloud-based computing system can include a cloud computing platform as described above. The cloud computing platform can include a multitenant database system that is configurable to provide applications and services to a plurality of clients. Each client can be, for example, a tenant or organization of the cloud computing platform.

FIG. 1 is a schematic block diagram of an example of a computing environment 100 in accordance with the disclosed embodiments. As illustrated in FIG. 1, the computing environment 100 can include an external user interface (UI) source platform 110, a first application platform 120-1, and a second application platform 120-N. In this embodiment, the first application platform 120-1 and the second application platform 120-N are independent of one another, and it should be appreciated that while two application platforms 120-1, 120-N are illustrated in FIG. 1 for purposes of illustration, the disclosed embodiments can be applied to work with any number of additional application platforms that are not illustrated for sake of simplicity.

The UI source platform 110 includes a shared, common data schema 112, data 113, UI source code 116, a compilation module 117, and external application programming interfaces (APIs) 114. The external APIs 114 t expose the common data schema 112 on the first application platform 120-1 and the second application platform 120-2. In accordance with the disclosed embodiments, the data 113 provided by the external APIs 114 and the user interface components provided by platform packages 118-1, 118-N have an implicit shared knowledge of the common data schema 112 that results in (or allows for) interoperability between them. As will be explained below, the APIs 114 can be bound to packages 118, and the UI components from UI source code 116 are designed to pair with the learner APIs 114. The common data schema 112 specifies the names and data types of the attributes for the learning related entities. The shared knowledge of the common data schema 112 between the UI components 116 and the APIs 114 allows them to be composed to build custom learner experiences. To explain further, the common data schema 112 is “shared by” the UI components 116 and the APIs 114 to assure that the learning data provided by the APIs 114 is compatible with the UI components 116. Without a shared common data schema 112 there is no assurance that the APIs 114 provide all the information needed to render the UI components 116. It also eliminates the need to reformat the data.

As will be explained below, the UI source platform 110 can facilitate the integration of external data 113 (that includes content and contextual user information) and UI components (via platform packages 118-1, 118-N) to create a custom user experience within the context of an application 128-1 provided by the first application platform 120-1 and to create another, different custom user experience within the context of another application 128-N provided by the second application platform 120-N.

A user interface can include any number of user interface components, which can collectively be described as an atomic composable user interface. The user interface components can include, but are not limited to, visual representations of entities, lists of the entities, summaries of the entities, detailed learning components, evaluation components, content bricks that provide summary level detail of the learning, etc. The user interface source platform 110 can perform a transform/compilation process to generate one or more platform packages 118-1 . . . 118-N of user interface components (or “bundle” of UI components). One platform package 118-1 (or UI package 118-1) is compatible for usage on the first application platform 120-1, whereas another platform package 128-N (or UI package 118-2) is compatible for usage on the second application platform 120-N. In most cases, the platform packages 118 that are generated are different as will be described below (e.g., each platform package is compatible for usage on a particular application platform and specifically compiled for interoperability with that particular application platform). In addition, it should be appreciated that the user interface source platform 110 can generate any number of additional platform packages (not shown) of user interface components that are compatible for usage on other platforms that are not illustrated in FIG. 1.

To generate platform package(s), in one embodiment, the user interface source platform 110 can include a compilation module 117 that includes a compiler and bundler. The compiler is configured to transform (e.g., transpile) UI source code 116 of user interface components for usage on the first application platform 120-1. The user interface components are specific to the common data schema 112 shared with the external APIs 114. In one embodiment, the compiler takes source code and assets as input and provides transformed or transpiled source code and assets as output. The bundler is configured to generate platform packages 118 of user interface components (or “bundles” of UI components) that are compatible for usage on the first application platform 120-1. The platform package 118-1 of user interface components is specific to the first application platform 120-1, meaning that a particular application platform may have specific packaging specifications that the platform package 118-1 must comply with, whereas a different second application platform 120-N may have other, different specific packaging specifications that another platform package 118-N must comply with. In one embodiment, the bundler takes transpiled source code as input and provides a platform package of source code, assets, configuration and metadata as output. In one embodiment, each platform package can include, for example, transformed UI source code, assets, configuration and metadata of the user interface components.

Using the common schema 112 and UI source code 116, the user interface source platform 110 can generate two separate platform packages 118-1, 118-N for the first application platform 120-1 and the second application platform 120-2, respectively. In most cases, the two platform packages 118-1, 118-N that are generated are different, but in some cases could be the same or similar. To explain further, each platform package 118 is compatible for usage on a particular application platform and specifically compiled for interoperability with that particular application platform, and therefore, in some cases, if two application platforms are different then their respective platform packages may be different.

The external APIs 114 of the user interface source platform 110 are consumed by both the first application platform 120-1 and the second application platform 120-2. The external APIs 114 provide (or export) data 113 to the first application platform 120-1 and the second application platform 120-N.

The user interface source platform 110 is configured to export data 113 (via the external API 114) and the platform package 118-1 to the first application platform 120-1, and to export data 113 (via the external API 114) and the platform package 118-N to the second application platform 120-N. This way, the first application platform 120-1 can receive the data 113 and the package 118-1 from the UI source platform 110, whereas the second application platform 120-N can receive the data 113 and a separate/different platform package 118-N from the same UI source platform 110.

In some embodiments, a platform integration module 126-1 of the first application platform 120-1 composes the data 113 provided via the external APIs 114 and the user interface components from the platform package 118-1 to provide to provide application code for a custom user experience within the context of the application 128-1 provided by the first application platform 120-1. Depending on the implementation, the platform integration module 126-1 of the first application platform 120-1 may compose the APIs 114 and the user interface components from the platform package 118-1 via one or more of: application code, composition in an experience builder by an administrator, or an automation application.

Similarly, a platform integration module 126-N of the second application platform 120-N composes the data 113 provided via the external APIs 114 and the user interface components from the platform package 118-N to provide to provide application code for a custom user experience within the context of the application 128-N provided by the second application platform 120-N. Depending on the implementation, the platform integration module 126-N of the second application platform 120-N may compose the application interfaces and the user interface components from the platform package 118-N via one or more of: application code, composition in an experience builder by an administrator, or an automation application, as will be explained in greater detail below.

FIG. 2 is a schematic block diagram of another example of a cloud computing environment 200 for implementing a learning platform architecture (LPA) in accordance with the disclosed embodiments. As illustrated in FIG. 2, the cloud computing environment 200 (or “cloud-based computing system”) can include an external learner user interface (UI) platform 210, a target cloud computing platform 220-1, an external learning platform 220-2, and learner application programming interfaces (APIs) 214 that expose a common learning data schema 212 on the cloud computing platform 220-1. In one non-limiting embodiment, the cloud computing platform 220-1 can include a multitenant database system that is configurable to provide applications and services to a plurality of clients, where each client can be, for example, a tenant or organization of the cloud computing platform 220-1. Examples of a cloud computing platform like this will be described below with reference to FIGS. 6-9B. A specific, non-limiting example of such a cloud computing platform is Salesforce.com®; however, it should be appreciated that the cloud computing platform could include other platforms such as SAP®, Oracle®, etc. Non-limiting examples of external learning platform 220-2 can include, for example, Trailhead.com®, Microsoft Learn®, Microsoft CRM Dynamics®, SAP Concur Expenses®, Workday HRMS®, Cornerstone On-Demand®, Instructure®, and Lessonly®, etc. It should be appreciated that the disclosed embodiments can be applied to work with any number of application platforms and that two (i.e., the cloud computing platform 220-1 and the external learning platform 220-2) are illustrated in FIG. 2 for purposes of illustration.

The UI platform 210 can include a common learning data schema 212, learning data 213, learner APIs 214, source code of UI components 216, and a compiler and bundler 217. As will be explained below, the UI platform 210 provides external learning data 213 (that includes learning content and contextual user information) to the application platforms 220-1, 220-2 via the learner APIs 214, and provides UI components 216 to the application platforms 220-1, 220-2 via separate/different platform packages 218 that it generates for the application platforms 220-1, 220-2. Examples of contextual user information are described above, and will not be repeated here for sake of brevity.

In accordance with the disclosed embodiments, the data provided by the learner APIs 214 and the user interface components 216 provided by the platform packages 218 have an implicit shared knowledge of the common learning data schema 212 that results in (or allows for) interoperability between them (e.g., the attributes and data types for the learner API entities 214 conform to the type definitions of the UI components 216). As will be explained below, the learner APIs 214 can be bound to packages 218, and the UI components 216 are designed to pair with the learner APIs 214. The common learning data schema 212 specifies the names and data types of the attributes for the learning related entities. The shared knowledge of the common learning data schema 212 between the UI components 216 and the learner APIs 214 allows them to be composed to build custom learner experiences. To explain further, the common learning data schema 212 is “shared by” the UI components 216 and the APIs 214 to assure that the learning data provided by the APIs 214 is compatible with the UI components 216. Without a shared common learning data schema 212 there is no assurance that the APIs 214 provide all the information needed to render the UI components 216. It also eliminates the need to reformat the data.

For example, the UI platform 210 can facilitate the integration of external learning data 213 (provided via the learner APIs 214) and UI components 216 (provided via a platform package 218) to create a custom learner experience within the context of an application 228-1 provided by the cloud computing platform 220-1. Similarly, the UI platform 210 can facilitate the integration of external learning data 213 and UI components 216 (provided via another platform package 218) to create another custom learner experience within the context of an external learning application 228-2 provided by the external learning platform 220-2.

A user interface of the can be made up of any number of user interface components, which can collectively be described as an atomic composable user interface. As will be explained below, the user interface components are composable to create a standardized, packaged, learning experience applicable to many applications within the cloud computing platform. The user interface components are specific to the common learning data schema 212 shared with the learner APIs 214. The user interface components can include, but are not limited to, visual representation(s) of entities, lists of entities, subsets of entities, summaries of entities, actions that can be performed on said entities, etc.

The user interface platform 210 can perform a transform/compilation process to generate packages 218 of user interface components (or “bundle” of UI components). One package 218 is compatible for usage on the cloud computing platform 220-1 (e.g., is specifically compiled for interoperability with the cloud computing platform 220-1 and uses formats that are compatible with application code of the cloud computing platform 220-1), whereas another package 218 is compatible for usage on the external learning platform 220-2 (e.g., is specifically compiled for interoperability with the external learning platform 220-2 and uses formats that are compatible with application code of the external learning platform 220-2). In addition, the user interface platform 210 can generate additional packages of user interface components that are compatible for usage on other platforms that are not illustrated in FIG. 2.

To generate package(s), in one embodiment, the user interface platform 210 can include a compiler and bundler 217. Each platform 220-1, 220-2 may have its own/different expectations for UI packaging, and therefore, the platform specific compilation steps can vary from one platform 220-1 to another platform 220-2. In addition, the platform specific compilation steps can change as requirements or expectations for UI packaging of a platform 220 are developed or change. In general terms, the compiler 217 is a translator that takes the original source code written in a programming language as its input and repackages it to produce equivalent source code in the same or a different programming language for a respective target platform, such as cloud computing platform 220-1. The compiler 217 can transform (e.g., transpile) source code 216 of user interface components of a componentized learner user interface for usage on the cloud computing platform 220-1. For example, in one embodiment, the compiler 217 takes source code and assets as input and provides transpiled source code and assets as output.

The bundler 217 is configured to generate the package 218 of user interface components (or “bundle” of user interface components) that are compatible for usage on the cloud computing platform 220-1. The package 218 of user interface components is specific to the cloud computing platform 220-1, meaning that a particular cloud computing platform may have specific packaging specifications that the package 218 must comply with, whereas a different application platform (like the external learning platform 220-2) may have other, different specific packaging specifications that another platform package must comply with. In one embodiment, the bundler 217 takes transpiled source code as input and provides a package of source code, assets, configuration and metadata as output.

In one embodiment, the package 218 can include, for example, transformed source code, assets, configuration and metadata of the user interface components 216 that is specifically compiled for interoperability with the target platform it is being distributed to. The package can be a file or directory that specifically compiled for interoperability with the varies depending on the requirements of the particular target platform that a package 218 is being distributed to. For instance, the package can be a file or directory that is described, for instance, by a package.json file, xml files, manifest files, etc. In some non-limiting embodiments, the package 218 can include, for example, one or more of metadata, configuration, JavaScript, stylesheets, images, other artifacts of the packaging performed by the bundler 217, etc. For instance, the package 218 can include artifacts of a build process such as a manifest (e.g., list of items in the bundle), type references, source maps, labels, publishing information, metadata, etc.

The learner APIs 214 of the user interface platform 210 are consumed by both the cloud computing platform 220-1 and the external learning platform 220-2. The external APIs 114 provide (or export) learning data 213 to the cloud computing platform 220-1 and the external learning platform 220-2.

The user interface platform 210 is configured to export learning data 213 (via the learner API 214), and to export a package 218 to the cloud computing platform 220-1. In some non-limiting embodiments, the cloud computing platform 220-1 can include a platform compiler 224 that can transform (e.g., transpile) the source code 216 of the package 218 (or “user interface bundle”) into application code of the cloud computing platform 220-1. To explain further, the user interface components from the package 218 are built to work with any web browser; however, in some cases, the cloud computing platform 220-1 may need to compile the normal source code of the user interface components (provided in the package 218) first so that they can be utilized at the cloud computing platform 220-1. The platform compiler 224 can compile the package 218 directly within the cloud computing platform 220-1. The UI package 218 eliminates the need for a developer to create any markup or CSS. Learning data and learning UI can be imported and will work in the flow of the application.

In some non-limiting embodiments, the cloud computing platform 220-1 implements external learning entities 222 that allow for API integration with the cloud computing platform 220-1. The external learning entities 222 are instances of the learner APIs 214 that make the common learning data schema 212 available within the cloud computing platform 220-1. The external learning entities 222 can include learning data 213 (e.g., learning content and contextual user information), and have attributes expected by the user interface components. Non-limiting examples of learning content attributes can include, but are not limited to, for example, title, description, points, published date, path, image, estimated time, pretitle, pretitle link, type, etc. Non-limiting examples of contextual user information about the learning content can include, but are not limited to, for example, finished at, estimated time left, percentage complete, points earned, etc.

In some embodiments, a platform integration module 226 of the cloud computing platform 220-1 composes the learning data 213 provided via the learner APIs 214 and the user interface components from the package 218 to provide application code for a custom learner experience within the context of the application 228 provided by the cloud computing platform 220-1. For instance, in one non-limiting example, where a feature on the platform is a “Learning Homepage”, this UX may include lists of learning content (e.g., in progress, new, assignments, etc.). A platform developer would query the data for these learnings lists from the learning API. The platform developer would also import the UI for learning lists from the learning UI package. Then the developer would “compose” (create a list of lists in this case) these learning lists to represent the specified application UX. The platform developer would then provide the data to the UI.

Depending on the implementation, the platform integration module 226 of the cloud computing platform 220-1 may compose the learning data 213 provided via the learner APIs 214 and the user interface components from the package 218 via one or more of: application code, composition in an experience builder by an administrator, or via an automation application. For instance, in one implementation, the platform can have features that allow developers to build applications with code, in which case they use a platform UI package in their UI, and the API to provide data. In another implementation, the user interface components are composable, for example, in an experience builder by an administrator of an application within the cloud computing platform. For example, the platform can provide a builder that allows developers or customers to use a GUI to “piece together” an application from a platform UI package and API data. In yet another implementation, the platform can have intelligent capabilities for building applications from metadata criteria, in which case it evaluates UI and API metadata and creates an application.

Using the same learning data schema 212 and UI source code 216, the user interface UI platform 210 can generate a separate platform package for the external learning platform 220-2 that is configured for interoperability with the external learning platform 220-2. In most cases, the two platform packages that are generated for the cloud computing platform 220-1 and the external learning platform 220-2 are different, but in some cases could be the same or similar. The user interface UI platform 210 is configured to export learning data 213 (via the learner API 214) and another platform package 218 to the external learning platform 220-2. This way, the external learning platform 220-2 can receive the data 213 and a separate/different platform package from UI platform 210. The external learning platform 220-2 can then compose the learning data 213 provided via the learner APIs 214 and the user interface components from the package 218 to provide application code for a custom learner experience within the context of the application 228-2 provided by the cloud computing platform 220-1.

In some non-limiting embodiments, the user interface components provide an eventing function for communication with the cloud computing platform 220-1 that allows for integration with the application 228 provided by the cloud computing platform 220-1, and an eventing integration function for interacting with resources from the external learning platform 220-2 system within an iFrame 234. The eventing integration function allows for the external learning platform 220-2 to communicate with the application 228 provided by the cloud computing platform 220-1. Interactions within the iFrame can change state of the platform application 228-1. User interaction with iFrame 234 is communicated to host so that the host can perform actions keyed off the user interactions with the iFrame. The iFrame 234 is an external source (that may be part of the UI platform 210) that interacts with the platform app 228-1 through browser events. The iFrame 234 is tied into the platform 220-1 with the application code 226 that is implemented for a feature such that the behavior within the iFrame is integrated with the application 228-1 (and custom learner experience).

For example, when a user interacts with a link in a learning item that emits a browser event indicating the user clicked the link, then the application listens to that event and determines that the user wants to see the iFrame and transitions the screen to the iFrame. In one implementation, handlers are bound to specific user interactions, such as clicking a link, that leverage the browser postMessage API to send a payload to a web component in the application platform that encapsulates the iFrame. The web component in turn can trigger events within the platform.

FIG. 3 is a flowchart of a method 300 in accordance with the disclosed embodiments. The method 300 can integrating learning data (e.g., learning content and contextual user information) provided by an external learning platform to create a custom learner experience within the context of an application provided by a cloud computing platform in accordance with the disclosed embodiments. As illustrated in FIG. 3, the method 300 can be implemented in a cloud computing environment that can include an external learner user interface (UI) platform 310, a cloud computing platform 320-1, an external learning platform 320-2, and learner application programming interfaces (APIs) 314. It should be appreciated that the disclosed embodiments can be applied to work with any number of application platforms and that two application platforms (i.e., the cloud computing platform 320-1 and the external learning platform 320-2) are illustrated in FIG. 3 for purposes of illustration. With respect to FIG. 3, the steps of the method shown are not necessarily limiting. Steps can be added, omitted, and/or performed simultaneously without departing from the scope of the appended claims. The method may include any number of additional or alternative tasks, and the tasks shown need not be performed in the illustrated order. The method may be incorporated into a more comprehensive procedure or process having additional functionality not described in detail herein. Moreover, one or more of the tasks shown could potentially be omitted from an embodiment of the method as long as the intended overall functionality remains intact. Further, the method is computer-implemented in that various tasks or steps that are performed in connection with the method may be performed by software, hardware, firmware, or any combination thereof. For illustrative purposes, the following description of each method may refer to elements mentioned above in connection with FIG. 2. In certain embodiments, some or all steps of this process, and/or substantially equivalent steps, are performed by execution of processor-readable instructions stored or included on a processor-readable medium. For instance, in the description of FIG. 3 that follows, the external learner user interface (UI) platform 310, the cloud computing platform 320-1, the external learning platform 320-2, and the learner application programming interfaces APIs 314 can be described as performing various acts, tasks or steps, but it should be appreciated that this refers to processing system(s) of these entities executing instructions to perform those various acts, tasks or steps. Depending on the implementation, some of the processing system(s) can be centrally located, or distributed among a number of server systems that work together. Furthermore, in the description of FIG. 3, a particular example is described in which a platform performs certain actions by interacting with other elements of FIG. 3.

A user interface of the external learning platform 320-2 can be made up of any number of user interface components, which can collectively be described as an atomic composable user interface. Non-limiting examples of the user interface components are described above in conjunction with FIGS. 1 and 2. In accordance with the disclosed embodiments, the learner APIs 314 and the user interface components have an implicit shared knowledge of a common learning data schema that results in (or allows for) interoperability between them. The learner APIs 314 expose a common learning data schema on the cloud computing platform 320-1. As will be explained below, the cloud computing environment can help facilitate learning while in a flow of work within an application (not illustrated) provided by the application platform 326 by integrating learning data (that includes learning content and contextual user information) provided by the external learning platform 320-2 to create a custom learner experience within the context of the application (not illustrated) provided by the application platform 326. In this regard, a work flow is a depiction of a sequence of operations. Learning in the flow of work means introducing learning tasks sometime in the sequence to integrate learning and productivity tasks for greater success of completing the task more accurately. It's desirable to keep the user of the application in their flow of work by not redirecting them outside of their core productivity tools and enable them to integrate work and learning within the same tasks such as selling or providing a service to a customer.

As will be described below, the user interface platform 310 can perform a transform/compilation process to generate one or more packages of user interface components (or “bundle” of UI components). In the embodiment that is illustrated in FIG. 3, one package 345 is compatible for usage on the cloud computing platform 320-1, whereas another package (not shown) could be generated that is compatible for usage on the external learning platform 320-2. In addition, the user interface platform 310 can generate additional packages of user interface components that are compatible for usage on other platforms that are not illustrated in FIG. 3. In one embodiment, the user interface platform 310 can include a compiler and a bundler that are not illustrated.

At 330, to generate one or more package(s), the compiler component of the user interface platform 310 can transform (e.g., transpile) source code 316 of user interface components (of a componentized learner user interface) for usage on the cloud computing platform 320-1. The user interface components are specific to the common learning data schema shared with the learner APIs 314. In one embodiment, the compiler takes source code and assets 316 as input and transforms them to provide transpiled source code and assets as output.

At 340, the bundler component of the user interface platform 310 can generate the package 345 of user interface components (or “bundle” of UI components) that are compatible for usage on the cloud computing platform 320-1. In this embodiment, the package 345 of user interface components is specific to the cloud computing platform 320-1, meaning that a particular cloud computing platform has specific packaging specifications that the package 345 is to comply with, whereas a different application platform 320-2 may have other, different specific packaging specifications that another platform package must comply with. In one non-limiting embodiment, the bundler takes transpiled source code as input and provides a package of source code, assets, configuration and metadata as output. In one embodiment, the package 345 can include, for example, transformed source code 316, assets, configuration and metadata of the user interface components. In some non-limiting embodiments, the package 345 comprises source code 316 specifically compiled for interoperability with the cloud computing platform 320-1. In one non-limiting implementation, the package 345 can include, for example, metadata, configuration, JavaScript, stylesheets, images, and other artifacts of the packaging performed by the bundler.

At 345, the user interface platform 310 exports the package 345 to the cloud computing platform 320-1. In some embodiments, the cloud computing platform 320-1 can then transform (e.g., transpile) the source code 316 of the package 345 (or “user interface bundle”) into application code of the cloud computing platform 320-1. To explain further, the user interface components from the package 345 can be built to work with any web browser; however, in some cases, the cloud computing platform 320-1 may need to compile the normal source code of the user interface components provided in the package 345 first so that they can be utilized at the cloud computing platform 320-1.

The cloud computing platform 320-1 can implement instances of the learner APIs 314 that make the common learning data schema available within the cloud computing platform 320-1. Learning data can have attributes expected by the user interface components. At 350, the cloud computing platform 320-1 composes the learning data 313 provided via the learner APIs 314 and the user interface components from the package 345 to provide application code a custom learner experience (not illustrated) provided by the application platform 326 within the context of the application (not illustrated) provided by the application platform 326 of the cloud computing platform 320-1. As noted above, depending on the implementation, the cloud computing platform 320-1 may compose the learner application interfaces 314 and the user interface components from the package 345 via one or more of: application code, composition in an experience builder by an administrator, or an automation application.

Thus, without having to leave the application that is provided by the application platform 326, a user experience from the external learning platform 320-2 (e.g., Trailhead) can be imported so that it is shared between an external learning application that is provided by the external learning platform 320-2 and the cloud computing platform 320-1, while retaining same look and feel, application functionality, information architecture, interaction behavior (events), style and/or branding of the external learning application provided by the external learning platform 320-2.

FIG. 4 is a set of screenshots 400 is a set of screenshots 410, 420, 430, 440 that illustrates various user interfaces that are presented to an end user during a user learner experience when interacting with the for implementing a learning platform architecture (LPA) of FIG. 2. In FIG. 4, the screenshot 414 shows an example of different permutations of UI components that are exported as part of a UI package 218 in one non-limiting implementation. The screenshot 410 represents an example of a learning list design pattern created from the UI components of the UI package 218 of FIG. 2, where 416 represents one subset of the UI components 416 that are displayed as part of the learning list design pattern. The screenshot 412 represents a common learning data schema 212 of FIG. 2 for an API data model that is described in detail in U.S. Provisional Application No. 63/080,608, filed Sep. 18, 2020, which is incorporated herein by reference in its entirety. The underlying learning data schema 212 in screenshot 412 is exposed to an API that can be consumed by the UI components in screenshot 414 such as lists, summaries, tiles, etc. The manifestation of these UI elements is shown in screenshot 416 which take the underlying schema, exposed in the UI components and render them in an orderly fashion within the app container that is screenshot 410. The screenshot 420 is a UI of an application 228-1 that illustrates a custom learner experience as experienced at the target cloud computing platform 220-1 in one non-limiting implementation, whereas the screenshot 430 represents is a UI of an application 228-2 (e.g., an original learning application) that illustrates a custom learner experience as experienced at the external learning platform 220-2 in one non-limiting implementation. The custom learner experience as experienced at the target cloud computing platform 220-1 shows an Iframe implementation of learning content 422 generated at the at the target cloud computing platform 220-1. The custom learner experience as experienced at the external learning platform 220-2 shows the same learning content that is displayed as custom learner experience as experienced at the target cloud computing platform 220-1, but shows how the learning content would be displayed differently on the external learning platform 220-2.

FIG. 5 is a screenshot 500 of a user interface that shows the user interface elements 410, 414 of FIG. 4 in greater resolution in accordance with the disclosed embodiments. 414 illustrates the UI components that are exported as part of a UI package 218 in greater resolution. As illustrated, the UI components include a UI component having a core tile format most often used in a grid, a UI component having a brick format (e.g., an elongated tile that is most often stacked to form a path), a UI component having a compact brick format, a UI component having a list format, etc. The user interface 410 illustrates a search results list view 416 that can be displayed. These content pieces map to a UI pattern called Content List Item using many different formats (e.g., tile format, brick format, compact brick format, list format, etc.) as shown at 414. The different formats 414 can be constructed to include various entities from a data model 412 and presented in different formats at 416.

As noted above, in one implementation, the technologies described above with reference to FIGS. 1-5 can be used in conjunction with a core cloud computing platform, such as a multitenant database system, that provides applications and services to multiple tenants or organizations via the cloud computing platform. One example of such a system will now be described below with reference to FIGS. 6-10.

FIG. 6 is a schematic block diagram of an example of a multi-tenant computing environment in which features of the disclosed embodiments can be implemented in accordance with the disclosed embodiments. As shown in FIG. 6, an exemplary cloud-based solution may be implemented in the context of a multi-tenant system 600 including a server 602 that supports applications 628 based upon data 632 from a database 630 that may be shared between multiple tenants, organizations, or enterprises, referred to herein as a multi-tenant database. The multi-tenant system 600 can be shared by many different organizations, and handles the storage of, and access to, different metadata, objects, data and applications across disparate organizations. In one embodiment, the multi-tenant system 600 can be part of a database system, such as a multi-tenant database system.

The multi-tenant system 600 can provide applications and services and store data for any number of organizations. Each organization is a source of metadata and data associated with that metadata that collectively make up an application. In one implementation, the metadata can include customized content of the organization (e.g., customizations done to an instance that define business logic and processes for an organization). Some non-limiting examples of metadata can include, for example, customized content that describes a build and functionality of objects (or tables), tabs, fields (or columns), permissions, classes, pages (e.g., Apex pages), triggers, controllers, sites, communities, workflow rules, automation rules and processes, etc. Data is associated with metadata to create an application. Data can be stored as one or more objects, where each object holds particular records for an organization. As such, data can include records (or user content) that are held by one or more objects.

The multi-tenant system 600 allows users of user systems 640 to establish a communicative connection to the multi-tenant system 600 over a network 645 such as the Internet or any type of network described herein. Based on a user's interaction with a user system 640, the application platform 610 accesses an organization's data (e.g., records held by an object) and metadata that is stored at one or more database systems 630, and provides the user system 640 with access to applications based on that data and metadata. These applications are executed or run in a process space of the application platform 610 will be described in greater detail below. The user system 640 and various other user systems (not illustrated) can interact with the applications provided by the multi-tenant system 600. The multi-tenant system 600 is configured to handle requests for any user associated with any organization that is a tenant of the system. Data and services generated by the various applications 628 are provided via a network 645 to any number of user systems 640, such as desktops, laptops, tablets, smartphones or other client devices, Google Glass™, and any other computing device implemented in an automobile, aircraft, television, or other business or consumer electronic device or system, including web clients.

Each application 628 is suitably generated at run-time (or on-demand) using a common application platform 610 that securely provides access to the data 632 in the database 630 for each of the various tenant organizations subscribing to the system 600. The application platform 610 has access to one or more database systems 630 that store information (e.g., data and metadata) for a number of different organizations including user information, organization information, custom information, etc. The database systems 630 can include a multi-tenant database system 630 as described with reference to FIG. 6, as well as other databases or sources of information that are external to the multi-tenant database system 630 of FIG. 6. In accordance with one non-limiting example, the service cloud 600 is implemented in the form of an on-demand multi-tenant customer relationship management (CRM) system that can support any number of authenticated users for a plurality of tenants.

As used herein, a “tenant” or an “organization” should be understood as referring to a group of one or more users (typically employees) that share access to common subset of the data within the multi-tenant database 630. In this regard, each tenant includes one or more users and/or groups associated with, authorized by, or otherwise belonging to that respective tenant. Stated another way, each respective user within the multi-tenant system 600 is associated with, assigned to, or otherwise belongs to a particular one of the plurality of enterprises supported by the system 600.

Each enterprise tenant may represent a company, corporate department, business or legal organization, and/or any other entities that maintain data for particular sets of users (such as their respective employees or customers) within the multi-tenant system 600. Although multiple tenants may share access to the server 602 and the database 630, the particular data and services provided from the server 602 to each tenant can be securely isolated from those provided to other tenants. The multi-tenant architecture therefore allows different sets of users to share functionality and hardware resources without necessarily sharing any of the data 632 belonging to or otherwise associated with other organizations.

The multi-tenant database 630 may be a repository or other data storage system capable of storing and managing the data 632 associated with any number of tenant organizations. The database 630 may be implemented using conventional database server hardware. In various embodiments, the database 630 shares processing hardware 604 with the server 602. In other embodiments, the database 630 is implemented using separate physical and/or virtual database server hardware that communicates with the server 602 to perform the various functions described herein.

In an exemplary embodiment, the database 630 includes a database management system or other equivalent software capable of determining an optimal query plan for retrieving and providing a particular subset of the data 632 to an instance of application (or virtual application) 628 in response to a query initiated or otherwise provided by an application 628, as described in greater detail below. The multi-tenant database 630 may alternatively be referred to herein as an on-demand database, in that the database 630 provides (or is available to provide) data at run-time to on-demand virtual applications 628 generated by the application platform 610, as described in greater detail below.

In practice, the data 632 may be organized and formatted in any manner to support the application platform 610. In various embodiments, the data 632 is suitably organized into a relatively small number of large data tables to maintain a semi-amorphous “heap”-type format. The data 632 can then be organized as needed for a particular virtual application 628. In various embodiments, conventional data relationships are established using any number of pivot tables 634 that establish indexing, uniqueness, relationships between entities, and/or other aspects of conventional database organization as desired. Further data manipulation and report formatting is generally performed at run-time using a variety of metadata constructs. Metadata within a universal data directory (UDD) 636, for example, can be used to describe any number of forms, reports, workflows, user access privileges, business logic and other constructs that are common to multiple tenants.

Tenant-specific formatting, functions and other constructs may be maintained as tenant-specific metadata 638 for each tenant, as desired. Rather than forcing the data 632 into an inflexible global structure that is common to all tenants and applications, the database 630 is organized to be relatively amorphous, with the pivot tables 634 and the metadata 638 providing additional structure on an as-needed basis. To that end, the application platform 610 suitably uses the pivot tables 634 and/or the metadata 638 to generate “virtual” components of the virtual applications 628 to logically obtain, process, and present the relatively amorphous data 632 from the database 630.

The server 602 may be implemented using one or more actual and/or virtual computing systems that collectively provide the dynamic application platform 610 for generating the virtual applications 628. For example, the server 602 may be implemented using a cluster of actual and/or virtual servers operating in conjunction with each other, typically in association with conventional network communications, cluster management, load balancing and other features as appropriate. The server 602 operates with any sort of conventional processing hardware 604, such as a processor 605, memory 606, input/output features 607 and the like. The input/output features 607 generally represent the interface(s) to networks (e.g., to the network 645, or any other local area, wide area or other network), mass storage, display devices, data entry devices and/or the like.

The processor 605 may be implemented using any suitable processing system, such as one or more processors, controllers, microprocessors, microcontrollers, processing cores and/or other computing resources spread across any number of distributed or integrated systems, including any number of “cloud-based” or other virtual systems. The memory 606 represents any non-transitory short-term or long-term storage or other computer-readable media capable of storing programming instructions for execution on the processor 605, including any sort of random-access memory (RAM), read only memory (ROM), flash memory, magnetic or optical mass storage, and/or the like. The computer-executable programming instructions, when read and executed by the server 602 and/or processor 605, cause the server 602 and/or processor 605 to create, generate, or otherwise facilitate the application platform 610 and/or virtual applications 628 and perform one or more additional tasks, operations, functions, and/or processes described herein. It should be noted that the memory 606 represents one suitable implementation of such computer-readable media, and alternatively or additionally, the server 602 could receive and cooperate with external computer-readable media that is realized as a portable or mobile component or platform, e.g., a portable hard drive, a USB flash drive, an optical disc, or the like.

The server 602, application platform 610 and database systems 630 can be part of one backend system. Although not illustrated, the multi-tenant system 600 can include other backend systems that can include one or more servers that work in conjunction with one or more databases and/or data processing components, and the application platform 610 can access the other backend systems.

The multi-tenant system 600 includes one or more user systems 640 that can access various applications provided by the application platform 610. The application platform 610 is a cloud-based user interface. The application platform 610 can be any sort of software application or other data processing engine that generates the virtual applications 628 that provide data and/or services to the user systems 640. In a typical embodiment, the application platform 610 gains access to processing resources, communications interfaces and other features of the processing hardware 604 using any sort of conventional or proprietary operating system 608. The virtual applications 628 are typically generated at run-time in response to input received from the user systems 640. For the illustrated embodiment, the application platform 610 includes a bulk data processing engine 612, a query generator 614, a search engine 616 that provides text indexing and other search functionality, and a runtime application generator 620. Each of these features may be implemented as a separate process or other module, and many equivalent embodiments could include different and/or additional features, components or other modules as desired.

The runtime application generator 620 dynamically builds and executes the virtual applications 628 in response to specific requests received from the user systems 640. The virtual applications 628 are typically constructed in accordance with the tenant-specific metadata 638, which describes the particular tables, reports, interfaces and/or other features of the particular application 628. In various embodiments, each virtual application 628 generates dynamic web content that can be served to a browser or other client program 642 associated with its user system 640, as appropriate.

The runtime application generator 620 suitably interacts with the query generator 614 to efficiently obtain multi-tenant data 632 from the database 630 as needed in response to input queries initiated or otherwise provided by users of the user systems 640. In a typical embodiment, the query generator 614 considers the identity of the user requesting a particular function (along with the user's associated tenant), and then builds and executes queries to the database 630 using system-wide metadata 636, tenant specific metadata 638, pivot tables 634, and/or any other available resources. The query generator 614 in this example therefore maintains security of the common database 630 by ensuring that queries are consistent with access privileges granted to the user and/or tenant that initiated the request.

With continued reference to FIG. 6, the data processing engine 612 performs bulk processing operations on the data 632 such as uploads or downloads, updates, online transaction processing, and/or the like. In many embodiments, less urgent bulk processing of the data 632 can be scheduled to occur as processing resources become available, thereby giving priority to more urgent data processing by the query generator 614, the search engine 616, the virtual applications 628, etc.

In exemplary embodiments, the application platform 610 is utilized to create and/or generate data-driven virtual applications 628 for the tenants that they support. Such virtual applications 628 may make use of interface features such as custom (or tenant-specific) screens 624, standard (or universal) screens 622 or the like. Any number of custom and/or standard objects 626 may also be available for integration into tenant-developed virtual applications 628. As used herein, “custom” should be understood as meaning that a respective object or application is tenant-specific (e.g., only available to users associated with a particular tenant in the multi-tenant system) or user-specific (e.g., only available to a particular subset of users within the multi-tenant system), whereas “standard” or “universal” applications or objects are available across multiple tenants in the multi-tenant system.

The data 632 associated with each virtual application 628 is provided to the database 630, as appropriate, and stored until it is requested or is otherwise needed, along with the metadata 638 that describes the particular features (e.g., reports, tables, functions, objects, fields, formulas, code, etc.) of that particular virtual application 628. For example, a virtual application 628 may include a number of objects 626 accessible to a tenant, wherein for each object 626 accessible to the tenant, information pertaining to its object type along with values for various fields associated with that respective object type are maintained as metadata 638 in the database 630. In this regard, the object type defines the structure (e.g., the formatting, functions and other constructs) of each respective object 626 and the various fields associated therewith.

Still referring to FIG. 6, the data and services provided by the server 602 can be retrieved using any sort of personal computer, mobile telephone, tablet or other network-enabled user system 640 on the network 645. In an exemplary embodiment, the user system 640 includes a display device, such as a monitor, screen, or another conventional electronic display capable of graphically presenting data and/or information retrieved from the multi-tenant database 630, as described in greater detail below.

Typically, the user operates a conventional browser application or other client program 642 executed by the user system 640 to contact the server 602 via the network 645 using a networking protocol, such as the hypertext transport protocol (HTTP) or the like. The user typically authenticates his or her identity to the server 602 to obtain a session identifier (“SessionID”) that identifies the user in subsequent communications with the server 602. When the identified user requests access to a virtual application 628, the runtime application generator 620 suitably creates the application at run time based upon the metadata 638, as appropriate. However, if a user chooses to manually upload an updated file (through either the web-based user interface or through an API), it will also be shared automatically with all of the users/devices that are designated for sharing.

As noted above, the virtual application 628 may contain JAVA®, ActiveX, or other content that can be presented using conventional client software running on the user system 640; other embodiments may simply provide dynamic web or other content that can be presented and viewed by the user, as desired. As described in greater detail below, the query generator 614 suitably obtains the requested subsets of data 632 from the database 630 as needed to populate the tables, reports or other features of the particular virtual application 628.

Objects and Records

In one embodiment, the multi-tenant database system 630 can store data in the form of records and customizations. As used herein, the term “record” can refer to a particular occurrence or instance of a data object that is created by a user or administrator of a database service and stored in a database system, for example, about a particular (actual or potential) business relationship or project. The data object can have a data structure defined by the database service (a standard object) or defined by a subscriber (custom object).

An object can refer to a structure used to store data and associated metadata along with a globally unique identifier (called an identity field) that allows for retrieval of the object. In one embodiment implementing a multi-tenant database, all of the records for the tenants have an identifier stored in a common table. Each object comprises a number of fields. A record has data fields that are defined by the structure of the object (e.g. fields of certain data types and purposes). An object is analogous to a database table, fields of an object are analogous to columns of the database table, and a record is analogous to a row in a database table. Data is stored as records of the object, which correspond to rows in a database. The terms “object” and “entity” are used interchangeably herein. Objects not only provide structure for storing data, but can also power the interface elements that allow users to interact with the data, such as tabs, the layout of fields on a page, and lists of related records. Objects can also have built-in support for features such as access management, validation, formulas, triggers, labels, notes and attachments, a track field history feature, security features, etc. Attributes of an object are described with metadata, making it easy to create and modify records either through a visual interface or programmatically.

A record can also have custom fields defined by a user. A field can be another record or include links thereto, thereby providing a parent-child relationship between the records. Customizations can include custom objects and fields, Apex Code, Visualforce, Workflow, etc.

Examples of objects include standard objects, custom objects, and external objects. A standard object can have a pre-defined data structure that is defined or specified by a database service or cloud computing platform. A standard object can be thought of as a default object. For example, in one embodiment, a standard object includes one or more pre-defined fields that are common for each organization that utilizes the cloud computing platform or database system or service.

A few non-limiting examples of different types of standard objects can include sales objects (e.g., accounts, contacts, opportunities, leads, campaigns, and other related objects); task and event objects (e.g., tasks and events and their related objects); support objects (e.g., cases and solutions and their related objects); salesforce knowledge objects (e.g., view and vote statistics, article versions, and other related objects); document, note, attachment objects and their related objects; user, sharing, and permission objects (e.g., users, profiles, and roles); profile and permission objects (e.g., users, profiles, permission sets, and related permission objects); record type objects (e.g., record types and business processes and their related objects); product and schedule objects (e.g., opportunities, products, and schedules); sharing and team selling objects (e.g., account teams, opportunity teams, and sharing objects); customizable forecasting objects (e.g., includes forecasts and related objects); forecasts objects (e.g., includes objects for collaborative forecasts); territory management (e.g., territories and related objects associated with territory management); process objects (e.g., approval processes and related objects); content objects (e.g., content and libraries and their related objects); chatter feed objects (e.g., objects related to feeds); badge and reward objects; feedback and performance cycle objects, etc. For example, a record can be for a business partner or potential business partner (e.g. a client, vendor, distributor, etc.) of the user, and can include an entire company, subsidiaries, or contacts at the company. As another example, a record can be a project that the user is working on, such as an opportunity (e.g. a possible sale) with an existing partner, or a project that the user is working on.

By contrast, a custom object can have a data structure that is defined, at least in part, by an organization or by a user/subscriber/admin of an organization. For example, a custom object can be an object that is custom defined by a user/subscriber/administrator of an organization, and includes one or more custom fields defined by the user or the particular organization for that custom object. Custom objects are custom database tables that allow an organization to store information unique to their organization. Custom objects can extend the functionality that standard objects provide.

In one embodiment, an object can be a relationship management entity having a record type defined within platform that includes a customer relationship management (CRM) database system for managing a company's relationships and interactions with their customers and potential customers. Examples of CRM entities can include, but are not limited to, an account, a case, an opportunity, a lead, a project, a contact, an order, a pricebook, a product, a solution, a report, a forecast, a user, etc. For instance, an opportunity can correspond to a sales prospect, marketing project, or other business-related activity with respect to which a user desires to collaborate with others.

An account object may include information about an organization or person (such as customers, competitors, and partners) involved with a particular business. Each object may be associated with fields. For example, an account object may include fields such as “company”, “zip”, “phone number”, “email address”, etc. A contact object may include contact information, where each contact may be an individual associated with an “account”. A contact object may include fields such as “first name”, “last name”, “phone number”, “accountID”, etc. The “accountID” field of the “contact” object may be the ID of the account that is the parent of the contact. An opportunities object includes information about a sale or a pending deal. An opportunities object may include fields such as “amount”, “accountID”, etc. The “accountID” field of the “opportunity” object may be the ID of the account that is associated with the opportunity. Each field may be associated with a field value. For example, a field value for the “zip” field may be “94105”.

External objects are objects that an organization creates that map to data stored outside the organization. External objects are like custom objects, but external object record data is stored outside the organization. For example, data that's stored on premises in an enterprise resource planning (ERP) system can be accessed as external objects in real time via web service callouts, instead of copying the data into the organization.

The following description is of one example of a system in which the features described above may be implemented. The components of the system described below are merely one example and should not be construed as limiting. The features described above may be implemented in any other type of computing environment, such as one with multiple servers, one with a single server, a multi-tenant server environment, a single-tenant server environment, or some combination of the above.

FIG. 7 shows a block diagram of an example of an environment 710 in which an on-demand database service can be used in accordance with some implementations. The environment 710 includes user systems 712, a network 714, a database system 716 (also referred to herein as a “cloud-based system”), a processor system 717, an application platform 718, a network interface 720, tenant database 722 for storing tenant data 723, system database 724 for storing system data 725, program code 726 for implementing various functions of the system 716, and process space 728 for executing database system processes and tenant-specific processes, such as running applications as part of an application hosting service. In some other implementations, environment 710 may not have all of these components or systems, or may have other components or systems instead of, or in addition to, those listed above.

In some implementations, the environment 710 is an environment in which an on-demand database service exists. An on-demand database service, such as that which can be implemented using the system 716, is a service that is made available to users outside of the enterprise(s) that own, maintain or provide access to the system 716. As described above, such users generally do not need to be concerned with building or maintaining the system 716. Instead, resources provided by the system 716 may be available for such users' use when the users need services provided by the system 716; that is, on the demand of the users. Some on-demand database services can store information from one or more tenants into tables of a common database image to form a multi-tenant database system (MTS). The term “multi-tenant database system” can refer to those systems in which various elements of hardware and software of a database system may be shared by one or more customers or tenants. For example, a given application server may simultaneously process requests for a great number of customers, and a given database table may store rows of data such as feed items for a potentially much greater number of customers. A database image can include one or more database objects. A relational database management system (RDBMS) or the equivalent can execute storage and retrieval of information against the database object(s).

Application platform 718 can be a framework that allows the applications of system 716 to execute, such as the hardware or software infrastructure of the system 716. In some implementations, the application platform 718 enables the creation, management and execution of one or more applications developed by the provider of the on-demand database service, users accessing the on-demand database service via user systems 712, or third-party application developers accessing the on-demand database service via user systems 712.

In some implementations, the system 716 implements a web-based customer relationship management (CRM) system. For example, in some such implementations, the system 716 includes application servers configured to implement and execute CRM software applications as well as provide related data, code, forms, renderable webpages and documents and other information to and from user systems 712 and to store to, and retrieve from, a database system related data, objects, and Webpage content. In some MTS implementations, data for multiple tenants may be stored in the same physical database object in tenant database 722. In some such implementations, tenant data is arranged in the storage medium(s) of tenant database 722 so that data of one tenant is kept logically separate from that of other tenants so that one tenant does not have access to another tenant's data, unless such data is expressly shared. The system 716 also implements applications other than, or in addition to, a CRM application. For example, the system 716 can provide tenant access to multiple hosted (standard and custom) applications, including a CRM application. User (or third-party developer) applications, which may or may not include CRM, may be supported by the application platform 718. The application platform 718 manages the creation and storage of the applications into one or more database objects and the execution of the applications in one or more virtual machines in the process space of the system 716.

According to some implementations, each system 716 is configured to provide webpages, forms, applications, data and media content to user (client) systems 712 to support the access by user systems 712 as tenants of system 716. As such, system 716 provides security mechanisms to keep each tenant's data separate unless the data is shared. If more than one MTS is used, they may be located in close proximity to one another (for example, in a server farm located in a single building or campus), or they may be distributed at locations remote from one another (for example, one or more servers located in city A and one or more servers located in city B). As used herein, each MTS could include one or more logically or physically connected servers distributed locally or across one or more geographic locations. Additionally, the term “server” is meant to refer to a computing device or system, including processing hardware and process space(s), an associated storage medium such as a memory device or database, and, in some instances, a database application (for example, OODBMS or RDBMS) as is well known in the art. It should also be understood that “server system” and “server” are often used interchangeably herein. Similarly, the database objects described herein can be implemented as part of a single database, a distributed database, a collection of distributed databases, a database with redundant online or offline backups or other redundancies, etc., and can include a distributed database or storage network and associated processing intelligence.

The network 714 can be or include any network or combination of networks of systems or devices that communicate with one another. For example, the network 714 can be or include any one or any combination of a LAN (local area network), 7AN (wide area network), telephone network, wireless network, cellular network, point-to-point network, star network, token ring network, hub network, or other appropriate configuration. The network 714 can include a TCP/IP (Transfer Control Protocol and Internet Protocol) network, such as the global internetwork of networks often referred to as the “Internet” (with a capital “I”). The Internet will be used in many of the examples herein. However, it should be understood that the networks that the disclosed implementations can use are not so limited, although TCP/IP is a frequently implemented protocol.

The user systems 712 can communicate with system 716 using TCP/IP and, at a higher network level, other common Internet protocols to communicate, such as HTTP, FTP, AFS, WAP, etc. In an example where HTTP is used, each user system 712 can include an HTTP client commonly referred to as a “web browser” or simply a “browser” for sending and receiving HTTP signals to and from an HTTP server of the system 716. Such an HTTP server can be implemented as the sole network interface 720 between the system 716 and the network 714, but other techniques can be used in addition to or instead of these techniques. In some implementations, the network interface 720 between the system 716 and the network 714 includes load sharing functionality, such as round-robin HTTP request distributors to balance loads and distribute incoming HTTP requests evenly over a number of servers. In MTS implementations, each of the servers can have access to the MTS data; however, other alternative configurations may be used instead.

The user systems 712 can be implemented as any computing device(s) or other data processing apparatus or systems usable by users to access the database system 716. For example, any of user systems 712 can be a desktop computer, a work station, a laptop computer, a tablet computer, a handheld computing device, a mobile cellular phone (for example, a “smartphone”), or any other Wi-Fi-enabled device, wireless access protocol (WAP)-enabled device, or other computing device capable of interfacing directly or indirectly to the Internet or other network. The terms “user system” and “computing device” are used interchangeably herein with one another and with the term “computer.” As described above, each user system 712 typically executes an HTTP client, for example, a web browsing (or simply “browsing”) program, such as a web browser based on the WebKit platform, Microsoft's Internet Explorer browser, Netscape's Navigator browser, Opera's browser, Mozilla's Firefox browser, or a WAP-enabled browser in the case of a cellular phone, PDA or other wireless device, or the like, allowing a user (for example, a subscriber of on-demand services provided by the system 716) of the user system 712 to access, process and view information, pages and applications available to it from the system 716 over the network 714.

Each user system 712 also typically includes one or more user input devices, such as a keyboard, a mouse, a trackball, a touch pad, a touch screen, a pen or stylus or the like, for interacting with a graphical user interface (GUI) provided by the browser on a display (for example, a monitor screen, liquid crystal display (LCD), light-emitting diode (LED) display, among other possibilities) of the user system 712 in conjunction with pages, forms, applications and other information provided by the system 716 or other systems or servers. For example, the user interface device can be used to access data and applications hosted by system 716, and to perform searches on stored data, and otherwise allow a user to interact with various GUI pages that may be presented to a user. As discussed above, implementations are suitable for use with the Internet, although other networks can be used instead of or in addition to the Internet, such as an intranet, an extranet, a virtual private network (VPN), a non-TCP/IP based network, any LAN or WAN or the like.

The users of user systems 712 may differ in their respective capacities, and the capacity of a particular user system 712 can be entirely determined by permissions (permission levels) for the current user of such user system. For example, where a salesperson is using a particular user system 712 to interact with the system 716, that user system can have the capacities allotted to the salesperson. However, while an administrator is using that user system 712 to interact with the system 716, that user system can have the capacities allotted to that administrator. Where a hierarchical role model is used, users at one permission level can have access to applications, data, and database information accessible by a lower permission level user, but may not have access to certain applications, database information, and data accessible by a user at a higher permission level. Thus, different users generally will have different capabilities with regard to accessing and modifying application and database information, depending on the users' respective security or permission levels (also referred to as “authorizations”).

According to some implementations, each user system 712 and some or all of its components are operator-configurable using applications, such as a browser, including computer code executed using a central processing unit (CPU) such as an Intel Pentium® processor or the like. Similarly, the system 716 (and additional instances of an MTS, where more than one is present) and all of its components can be operator-configurable using application(s) including computer code to run using the processor system 717, which may be implemented to include a CPU, which may include an Intel Pentium® processor or the like, or multiple CPUs.

The system 716 includes tangible computer-readable media having non-transitory instructions stored thereon/in that are executable by or used to program a server or other computing system (or collection of such servers or computing systems) to perform some of the implementation of processes described herein. For example, computer program code 726 can implement instructions for operating and configuring the system 716 to intercommunicate and to process webpages, applications and other data and media content as described herein. In some implementations, the computer code 726 can be downloadable and stored on a hard disk, but the entire program code, or portions thereof, also can be stored in any other volatile or non-volatile memory medium or device as is well known, such as a ROM or RAM, or provided on any media capable of storing program code, such as any type of rotating media including floppy disks, optical discs, digital versatile disks (DVD), compact disks (CD), microdrives, and magneto-optical disks, and magnetic or optical cards, nanosystems (including molecular memory ICs), or any other type of computer-readable medium or device suitable for storing instructions or data. Additionally, the entire program code, or portions thereof, may be transmitted and downloaded from a software source over a transmission medium, for example, over the Internet, or from another server, as is well known, or transmitted over any other existing network connection as is well known (for example, extranet, VPN, LAN, etc.) using any communication medium and protocols (for example, TCP/IP, HTTP, HTTPS, Ethernet, etc.) as are well known. It will also be appreciated that computer code for the disclosed implementations can be realized in any programming language that can be executed on a server or other computing system such as, for example, C, C++, HTML, any other markup language, JAVA®, JAVASCRIPT®, ActiveX®, any other scripting language, such as VBScript®, and many other programming languages as are well known may be used. (JAVA™ is a trademark of Sun Microsystems, Inc.).

FIG. 8 shows a block diagram of example implementations of elements of FIG. 7 and example interconnections between these elements according to some implementations. That is, FIG. 8 also illustrates environment 710, but FIG. 8, various elements of the system 716 and various interconnections between such elements are shown with more specificity according to some more specific implementations. Elements from FIG. 7 that are also shown in FIG. 8 will use the same reference numbers in FIG. 8 as were used in FIG. 7. Additionally, in FIG. 8, the user system 712 includes a processor system 812A, a memory system 812B, an input system 812C, and an output system 812D. The processor system 812A can include any suitable combination of one or more processors. The memory system 812B can include any suitable combination of one or more memory devices. The input system 812C can include any suitable combination of input devices, such as one or more touchscreen interfaces, keyboards, mice, trackballs, scanners, cameras, or interfaces to networks. The output system 812D can include any suitable combination of output devices, such as one or more display devices, printers, or interfaces to networks.

In FIG. 8, the network interface 720 of FIG. 7 is implemented as a set of HTTP application servers 8001-800N. Each application server 800, also referred to herein as an “app server,” is configured to communicate with tenant database 722 and the tenant data 823 therein, as well as system database 724 and the system data 825 therein, to serve requests received from the user systems 812. The tenant data 823 can be divided into individual tenant storage spaces 813, which can be physically or logically arranged or divided. Within each tenant storage space 813, tenant data 814 and application metadata 816 can similarly be allocated for each user. For example, a copy of a user's most recently used (MRU) items can be stored to tenant data 814. Similarly, a copy of MRU items for an entire organization that is a tenant can be stored to tenant storage space 813.

The process space 728 includes system process space 802, individual tenant process spaces 804 and a tenant management process space 810. The application platform 718 includes an application setup mechanism 838 that supports application developers' creation and management of applications. Such applications and others can be saved as metadata into tenant database 722 by save routines 836 for execution by subscribers as one or more tenant process spaces 804 managed by tenant management process 810, for example. Invocations to such applications can be coded using PL/SOQL 834, which provides a programming language style interface extension to API 832. A detailed description of some PL/SOQL language implementations is discussed in commonly assigned U.S. Pat. No. 7,730,478, titled METHOD AND SYSTEM FOR ALLOWING ACCESS TO DEVELOPED APPLICATIONS VIA A MULTI-TENANT ON-DEMAND DATABASE SERVICE, by Craig Weissman, issued on Jun. 1, 2010, and hereby incorporated by reference in its entirety and for all purposes. Invocations to applications can be detected by one or more system processes, which manage retrieving application metadata 816 for the subscriber making the invocation and executing the metadata as an application in a virtual machine.

The system 716 of FIG. 8 also includes a user interface (UI) 830 and an application programming interface (API) 832 to system 716 resident processes to users or developers at user systems 812. In some other implementations, the environment 710 may not have the same elements as those listed above or may have other elements instead of, or in addition to, those listed above.

Each application server 800 can be communicably coupled with tenant database 722 and system database 724, for example, having access to tenant data 823 and system data 825, respectively, via a different network connection. For example, one application server 8001 can be coupled via the network 714 (for example, the Internet), another application server 800N can be coupled via a direct network link, and another application server (not illustrated) can be coupled by yet a different network connection. Transfer Control Protocol and Internet Protocol (TCP/IP) are examples of typical protocols that can be used for communicating between application servers 800 and the system 716. However, it will be apparent to one skilled in the art that other transport protocols can be used to optimize the system 716 depending on the network interconnections used.

In some implementations, each application server 800 is configured to handle requests for any user associated with any organization that is a tenant of the system 716. Because it can be desirable to be able to add and remove application servers 800 from the server pool at any time and for various reasons, in some implementations there is no server affinity for a user or organization to a specific application server 800. In some such implementations, an interface system implementing a load balancing function (for example, an F5 Big-IP load balancer) is communicably coupled between the application servers 800 and the user systems 812 to distribute requests to the application servers 800. In one implementation, the load balancer uses a least-connections algorithm to route user requests to the application servers 800. Other examples of load balancing algorithms, such as round robin and observed-response-time, also can be used. For example, in some instances, three consecutive requests from the same user could hit three different application servers 800, and three requests from different users could hit the same application server 800. In this manner, by way of example, system 716 can be a multi-tenant system in which system 716 handles storage of, and access to, different objects, data and applications across disparate users and organizations.

In one example storage use case, one tenant can be a company that employs a sales force where each salesperson uses system 716 to manage aspects of their sales. A user can maintain contact data, leads data, customer follow-up data, performance data, goals and progress data, etc., all applicable to that user's personal sales process (for example, in tenant database 722). In an example of an MTS arrangement, because all of the data and the applications to access, view, modify, report, transmit, calculate, etc., can be maintained and accessed by a user system 812 having little more than network access, the user can manage his or her sales efforts and cycles from any of many different user systems. For example, when a salesperson is visiting a customer and the customer has Internet access in their lobby, the salesperson can obtain critical updates regarding that customer while waiting for the customer to arrive in the lobby.

While each user's data can be stored separately from other users' data regardless of the employers of each user, some data can be organization-wide data shared or accessible by several users or all of the users for a given organization that is a tenant. Thus, there can be some data structures managed by system 716 that are allocated at the tenant level while other data structures can be managed at the user level. Because an MTS can support multiple tenants including possible competitors, the MTS can have security protocols that keep data, applications, and application use separate. Also, because many tenants may opt for access to an MTS rather than maintain their own system, redundancy, up-time, and backup are additional functions that can be implemented in the MTS. In addition to user-specific data and tenant-specific data, the system 716 also can maintain system level data usable by multiple tenants or other data. Such system level data can include industry reports, news, postings, and the like that are sharable among tenants.

In some implementations, the user systems 812 (which also can be client systems) communicate with the application servers 800 to request and update system-level and tenant-level data from the system 716. Such requests and updates can involve sending one or more queries to tenant database 722 or system database 724. The system 716 (for example, an application server 800 in the system 716) can automatically generate one or more SQL statements (for example, one or more SQL queries) designed to access the desired information. System database 724 can generate query plans to access the requested data from the database. The term “query plan” generally refers to one or more operations used to access information in a database system.

Each database can generally be viewed as a collection of objects, such as a set of logical tables, containing data fitted into predefined or customizable categories. A “table” is one representation of a data object, and may be used herein to simplify the conceptual description of objects and custom objects according to some implementations. It should be understood that “table” and “object” may be used interchangeably herein. Each table generally contains one or more data categories logically arranged as columns or fields in a viewable schema. Each row or element of a table can contain an instance of data for each category defined by the fields. For example, a CRM database can include a table that describes a customer with fields for basic contact information such as name, address, phone number, fax number, etc. Another table can describe a purchase order, including fields for information such as customer, product, sale price, date, etc. In some MTS implementations, standard entity tables can be provided for use by all tenants. For CRM database applications, such standard entities can include tables for case, account, contact, lead, and opportunity data objects, each containing pre-defined fields. As used herein, the term “entity” also may be used interchangeably with “object” and “table.”

In some MTS implementations, tenants are allowed to create and store custom objects, or may be allowed to customize standard entities or objects, for example by creating custom fields for standard objects, including custom index fields. Commonly assigned U.S. Pat. No. 7,779,039, titled CUSTOM ENTITIES AND FIELDS IN A MULTI-TENANT DATABASE SYSTEM, by Weissman et al., issued on Aug. 17, 2010, and hereby incorporated by reference in its entirety and for all purposes, teaches systems and methods for creating custom objects as well as customizing standard objects in a multi-tenant database system. In some implementations, for example, all custom entity data rows are stored in a single multi-tenant physical table, which may contain multiple logical tables per organization. It is transparent to customers that their multiple “tables” are in fact stored in one large table or that their data may be stored in the same table as the data of other customers.

FIG. 9A shows a system diagram illustrating example architectural components of an on-demand database service environment 900 according to some implementations. A client machine communicably connected with the cloud 904, generally referring to one or more networks in combination, as described herein, can communicate with the on-demand database service environment 900 via one or more edge routers 908 and 912. A client machine can be any of the examples of user systems described above. The edge routers can communicate with one or more core switches 920 and 924 through a firewall 916. The core switches can communicate with a load balancer 928, which can distribute server load over different pods, such as the pods 940 and 944. The pods 940 and 944, which can each include one or more servers or other computing resources, can perform data processing and other operations used to provide on-demand services. Communication with the pods can be conducted via pod switches 932 and 936. Components of the on-demand database service environment can communicate with database storage 956 through a database firewall 948 and a database switch 952.

As shown in FIGS. 9A and 9B, accessing an on-demand database service environment can involve communications transmitted among a variety of different hardware or software components. Further, the on-demand database service environment 900 is a simplified representation of an actual on-demand database service environment. For example, while only one or two devices of each type are shown in FIGS. 9A and 9B, some implementations of an on-demand database service environment can include anywhere from one to several devices of each type. Also, the on-demand database service environment need not include each device shown in FIGS. 9A and 9B, or can include additional devices not shown in FIGS. 9A and 9B.

Additionally, it should be appreciated that one or more of the devices in the on-demand database service environment 900 can be implemented on the same physical device or on different hardware. Some devices can be implemented using hardware or a combination of hardware and software. Thus, terms such as “data processing apparatus,” “machine,” “server” and “device” as used herein are not limited to a single hardware device, rather references to these terms can include any suitable combination of hardware and software configured to provide the described functionality.

The cloud 904 is intended to refer to a data network or multiple data networks, often including the Internet. Client machines communicably connected with the cloud 904 can communicate with other components of the on-demand database service environment 900 to access services provided by the on-demand database service environment. For example, client machines can access the on-demand database service environment to retrieve, store, edit, or process information. In some implementations, the edge routers 908 and 912 route packets between the cloud 904 and other components of the on-demand database service environment 900. For example, the edge routers 908 and 912 can employ the Border Gateway Protocol (BGP). The BGP is the core routing protocol of the Internet. The edge routers 908 and 912 can maintain a table of IP networks or ‘prefixes’, which designate network reachability among autonomous systems on the Internet.

In some implementations, the firewall 916 can protect the inner components of the on-demand database service environment 900 from Internet traffic. The firewall 916 can block, permit, or deny access to the inner components of the on-demand database service environment 900 based upon a set of rules and other criteria. The firewall 916 can act as one or more of a packet filter, an application gateway, a stateful filter, a proxy server, or any other type of firewall.

In some implementations, the core switches 920 and 924 are high-capacity switches that transfer packets within the on-demand database service environment 900. The core switches 920 and 924 can be configured as network bridges that quickly route data between different components within the on-demand database service environment. In some implementations, the use of two or more core switches 920 and 924 can provide redundancy or reduced latency.

In some implementations, the pods 940 and 944 perform the core data processing and service functions provided by the on-demand database service environment. Each pod can include various types of hardware or software computing resources. An example of the pod architecture is discussed in greater detail with reference to FIG. 9B. In some implementations, communication between the pods 940 and 944 is conducted via the pod switches 932 and 936. The pod switches 932 and 936 can facilitate communication between the pods 940 and 944 and client machines communicably connected with the cloud 904, for example via core switches 920 and 924. Also, the pod switches 932 and 936 may facilitate communication between the pods 940 and 944 and the database storage 956. In some implementations, the load balancer 928 can distribute workload between the pods 940 and 944. Balancing the on-demand service requests between the pods can assist in improving the use of resources, increasing throughput, reducing response times, or reducing overhead. The load balancer 928 may include multilayer switches to analyze and forward traffic.

In some implementations, access to the database storage 956 is guarded by a database firewall 948. The database firewall 948 can act as a computer application firewall operating at the database application layer of a protocol stack. The database firewall 948 can protect the database storage 956 from application attacks such as structure query language (SQL) injection, database rootkits, and unauthorized information disclosure. In some implementations, the database firewall 948 includes a host using one or more forms of reverse proxy services to proxy traffic before passing it to a gateway router. The database firewall 948 can inspect the contents of database traffic and block certain content or database requests. The database firewall 948 can work on the SQL application level atop the TCP/IP stack, managing applications' connection to the database or SQL management interfaces as well as intercepting and enforcing packets traveling to or from a database network or application interface.

In some implementations, communication with the database storage 956 is conducted via the database switch 952. The multi-tenant database storage 956 can include more than one hardware or software components for handling database queries. Accordingly, the database switch 952 can direct database queries transmitted by other components of the on-demand database service environment (for example, the pods 940 and 944) to the correct components within the database storage 956. In some implementations, the database storage 956 is an on-demand database system shared by many different organizations as described above.

FIG. 9B shows a system diagram further illustrating example architectural components of an on-demand database service environment according to some implementations. The pod 944 can be used to render services to a user of the on-demand database service environment 900. In some implementations, each pod includes a variety of servers or other systems. The pod 944 includes one or more content batch servers 964, content search servers 968, query servers 982, file force servers 986, access control system (ACS) servers 980, batch servers 984, and app servers 988. The pod 944 also can include database instances 990, quick file systems (QFS) 992, and indexers 994. In some implementations, some or all communication between the servers in the pod 944 can be transmitted via the switch 936.

In some implementations, the app servers 988 include a hardware or software framework dedicated to the execution of procedures (for example, programs, routines, scripts) for supporting the construction of applications provided by the on-demand database service environment 900 via the pod 944. In some implementations, the hardware or software framework of an app server 988 is configured to execute operations of the services described herein, including performance of the blocks of various methods or processes described herein. In some alternative implementations, two or more app servers 988 can be included and cooperate to perform such methods, or one or more other servers described herein can be configured to perform the disclosed methods.

The content batch servers 964 can handle requests internal to the pod. Some such requests can be long-running or not tied to a particular customer. For example, the content batch servers 964 can handle requests related to log mining, cleanup work, and maintenance tasks. The content search servers 968 can provide query and indexer functions. For example, the functions provided by the content search servers 968 can allow users to search through content stored in the on-demand database service environment. The file force servers 986 can manage requests for information stored in the File force storage 998. The File force storage 998 can store information such as documents, images, and basic large objects (BLOBs). By managing requests for information using the file force servers 986, the image footprint on the database can be reduced. The query servers 982 can be used to retrieve information from one or more file storage systems. For example, the query system 982 can receive requests for information from the app servers 988 and transmit information queries to the NFS 996 located outside the pod.

The pod 944 can share a database instance 990 configured as a multi-tenant environment in which different organizations share access to the same database. Additionally, services rendered by the pod 944 may call upon various hardware or software resources. In some implementations, the ACS servers 980 control access to data, hardware resources, or software resources. In some implementations, the batch servers 984 process batch jobs, which are used to run tasks at specified times. For example, the batch servers 984 can transmit instructions to other servers, such as the app servers 988, to trigger the batch jobs.

In some implementations, the QFS 992 is an open source file storage system available from Sun Microsystems® of Santa Clara, Calif. The QFS can serve as a rapid-access file storage system for storing and accessing information available within the pod 944. The QFS 992 can support some volume management capabilities, allowing many disks to be grouped together into a file storage system. File storage system metadata can be kept on a separate set of disks, which can be useful for streaming applications where long disk seeks cannot be tolerated. Thus, the QFS system can communicate with one or more content search servers 968 or indexers 994 to identify, retrieve, move, or update data stored in the network file storage systems 996 or other storage systems.

In some implementations, one or more query servers 982 communicate with the NFS 996 to retrieve or update information stored outside of the pod 944. The NFS 996 can allow servers located in the pod 944 to access information to access files over a network in a manner similar to how local storage is accessed. In some implementations, queries from the query servers 982 are transmitted to the NFS 996 via the load balancer 928, which can distribute resource requests over various resources available in the on-demand database service environment. The NFS 996 also can communicate with the QFS 992 to update the information stored on the NFS 996 or to provide information to the QFS 992 for use by servers located within the pod 944.

In some implementations, the pod includes one or more database instances 990. The database instance 990 can transmit information to the QFS 992. When information is transmitted to the QFS, it can be available for use by servers within the pod 944 without using an additional database call. In some implementations, database information is transmitted to the indexer 994. Indexer 994 can provide an index of information available in the database 990 or QFS 992. The index information can be provided to file force servers 986 or the QFS 992.

FIG. 10 illustrates a diagrammatic representation of a machine in the exemplary form of a computer system 1000 within which a set of instructions, for causing the machine to perform any one or more of the methodologies discussed herein, may be executed. The system 1000 may be in the form of a computer system within which a set of instructions, for causing the machine to perform any one or more of the methodologies discussed herein, may be executed. In alternative embodiments, the machine may be connected (e.g., networked) to other machines in a LAN, an intranet, an extranet, or the Internet. The machine may operate in the capacity of a server machine in client-server network environment. The machine may be a personal computer (PC), a set-top box (STB), a server, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein. In one embodiment, computer system 1000 may represent application server 110.

The exemplary computer system 1000 includes a processing device (processor) 1002, a main memory 1004 (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM)), a static memory 1006 (e.g., flash memory, static random access memory (SRAM)), and a data storage device 1018, which communicate with each other via a bus 1030.

Processing device 1002 represents one or more general-purpose processing devices such as a microprocessor, central processing unit, or the like. More particularly, the processing device 1002 may be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, or a processor implementing other instruction sets or processors implementing a combination of instruction sets. The processing device 1002 may also be one or more special-purpose processing devices such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, or the like.

The computer system 1000 may further include a network interface device 1008. The computer system 1000 also may include a video display unit 1010 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)), an alphanumeric input device 1012 (e.g., a keyboard), a cursor control device 1014 (e.g., a mouse), and a signal generation device 1016 (e.g., a speaker).

The data storage device 1018 may include a computer-readable medium 1028 on which is stored one or more sets of instructions 1022 (e.g., instructions of in-memory buffer service 114) embodying any one or more of the methodologies or functions described herein. The instructions 1022 may also reside, completely or at least partially, within the main memory 1004 and/or within processing logic 1026 of the processing device 1002 during execution thereof by the computer system 1000, the main memory 1004 and the processing device 1002 also constituting computer-readable media. The instructions may further be transmitted or received over a network 1020 via the network interface device 1008.

While the computer-readable storage medium 1028 is shown in an exemplary embodiment to be a single medium, the term “computer-readable storage medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “computer-readable storage medium” shall also be taken to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present invention. The term “computer-readable storage medium” shall accordingly be taken to include, but not be limited to, solid-state memories, optical media, and magnetic media.

Particular embodiments may be implemented in a computer-readable storage medium (also referred to as a machine-readable storage medium) for use by or in connection with the instruction execution system, apparatus, system, or device. Particular embodiments can be implemented in the form of control logic in software or hardware or a combination of both. The control logic, when executed by one or more processors, may be operable to perform that which is described in particular embodiments.

A “processor,” “processor system,” or “processing system” includes any suitable hardware and/or software system, mechanism or component that processes data, signals or other information. A processor can include a system with a general-purpose central processing unit, multiple processing units, dedicated circuitry for achieving functionality, or other systems. Processing need not be limited to a geographic location, or have temporal limitations. For example, a processor can perform its functions in “real time,” “offline,” in a “batch mode,” etc. Portions of processing can be performed at different times and at different locations, by different (or the same) processing systems. A computer may be any processor in communication with a memory. The memory may be any suitable processor-readable storage medium, such as random-access memory (RAM), read-only memory (ROM), magnetic or optical disk, or other tangible media suitable for storing instructions for execution by the processor.

Particular embodiments may be implemented by using a programmed general purpose digital computer, by using application specific integrated circuits, programmable logic devices, field programmable gate arrays, optical, chemical, biological, quantum or nanoengineered systems, components and mechanisms may be used. In general, the functions of particular embodiments can be achieved by any means as is known in the art. Distributed, networked systems, components, and/or circuits can be used. Communication, or transfer, of data may be wired, wireless, or by any other means.

It will also be appreciated that one or more of the elements depicted in the drawings/figures can also be implemented in a more separated or integrated manner, or even removed or rendered as inoperable in certain cases, as is useful in accordance with a particular application. It is also within the spirit and scope to implement a program or code that can be stored in a machine-readable medium to permit a computer to perform any of the methods described above.

The preceding description sets forth numerous specific details such as examples of specific systems, components, methods, and so forth, in order to provide a good understanding of several embodiments of the present invention. It will be apparent to one skilled in the art, however, that at least some embodiments of the present invention may be practiced without these specific details. In other instances, well-known components or methods are not described in detail or are presented in simple block diagram format in order to avoid unnecessarily obscuring the present invention. Thus, the specific details set forth are merely exemplary. Particular implementations may vary from these exemplary details and still be contemplated to be within the scope of the present invention.

In the above description, numerous details are set forth. It will be apparent, however, to one of ordinary skill in the art having the benefit of this disclosure, that embodiments of the invention may be practiced without these specific details. In some instances, well-known structures and devices are shown in block diagram form, rather than in detail, in order to avoid obscuring the description.

Techniques and technologies may be described herein in terms of functional and/or logical block components, and with reference to symbolic representations of operations, processing tasks, and functions that may be performed by various computing components or devices. Such operations, tasks, and functions are sometimes referred to as being computer-executed, computerized, software-implemented, or computer-implemented. In this regard, it should be appreciated that the various block components shown in the figures may be realized by any number of hardware, software, and/or firmware components configured to perform the specified functions. For example, an embodiment of a system or a component may employ various integrated circuit components, e.g., memory elements, digital signal processing elements, logic elements, look-up tables, or the like, which may carry out a variety of functions under the control of one or more microprocessors or other control devices.

Some portions of the detailed description are presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of steps leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the above discussion, it is appreciated that throughout the description, discussions utilizing terms such as “determining,” “analyzing,” “identifying,” “adding,” “displaying,” “generating,” “querying,” “creating,” “selecting” or the like, refer to the actions and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (e.g., electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.

Embodiments of the invention also relate to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, or it may comprise a general purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a computer readable storage medium, such as, but not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, and magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, or any type of media suitable for storing electronic instructions.

The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Various general purpose systems may be used with programs in accordance with the teachings herein, or it may prove convenient to construct a more specialized apparatus to perform the required method steps. The required structure for a variety of these systems will appear from the description below. In addition, the present invention is not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the invention as described herein.

Any suitable programming language can be used to implement the routines of particular embodiments including C, C++, Java, assembly language, etc. Different programming techniques can be employed such as procedural or object oriented. The routines can execute on a single processing device or multiple processors. Although the steps, operations, or computations may be presented in a specific order, this order may be changed in different particular embodiments. In some particular embodiments, multiple steps shown as sequential in this specification can be performed at the same time.

As used in the description herein and throughout the claims that follow, “a”, “an”, and “the” includes plural references unless the context clearly dictates otherwise. Also, as used in the description herein and throughout the claims that follow, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.

The foregoing detailed description is merely illustrative in nature and is not intended to limit the embodiments of the subject matter or the application and uses of such embodiments. As used herein, the word “exemplary” means “serving as an example, instance, or illustration.” Any implementation described herein as exemplary is not necessarily to be construed as preferred or advantageous over other implementations. Furthermore, there is no intention to be bound by any expressed or implied theory presented in the preceding technical field, background, or detailed description.

While at least one exemplary embodiment has been presented in the foregoing detailed description, it should be appreciated that a vast number of variations exist. It should also be appreciated that the exemplary embodiment or embodiments described herein are not intended to limit the scope, applicability, or configuration of the claimed subject matter in any way. Rather, the foregoing detailed description will provide those skilled in the art with a convenient road map for implementing the described embodiment or embodiments. It should be understood that various changes can be made in the function and arrangement of elements without departing from the scope defined by the claims, which includes known equivalents and foreseeable equivalents at the time of filing this patent application. 

What is claimed is:
 1. A system for integrating learning data provided by an external learning platform to create a custom learner experience within a context of an application provided by a cloud computing platform, the system comprising: learner application programming interfaces (APIs) that expose a common learning data schema on the cloud computing platform; a user interface platform, comprising: a compiler configured to transform source code of user interface components of a componentized learner user interface for usage on the cloud computing platform, wherein the user interface components are specific to the common learning data schema shared with the learner APIs; and a bundler configured to: generate a package of user interface components that are compatible for usage on the cloud computing platform, and wherein the user interface platform is configured to export the package to the cloud computing platform, and wherein the cloud computing platform composes the learning data provided via the learner APIs and the user interface components from the package to provide a custom learner experience within the context of the application provided by the cloud computing platform.
 2. The system according to claim 1, wherein the learner APIs and the user interface components have an implicit shared knowledge of the common learning data schema that results in interoperability between them.
 3. The system according to claim 1, wherein the cloud computing platform composes the learning data provided via the learner application interfaces and the user interface components from the package via at least one of: application code; composition in an experience builder by an administrator; or an automation application.
 4. The system according to claim 1, wherein the cloud computing platform implements external learning entities that are instances of the learner APIs that make the common learning data schema available within the cloud computing platform.
 5. The system according to claim 4, wherein the external learning entities comprise the learning data, and have attributes expected by the user interface components.
 6. The system according to claim 1, wherein the package of user interface components is specific to the cloud computing platform, and comprises: transformed source code, assets, configuration, and metadata of the user interface components.
 7. The system according to claim 1, wherein the learning data comprises: learning content and contextual user information, and wherein the package comprises: source code specifically compiled for interoperability with the target cloud computing platform, and includes one or more of: metadata, configuration, JavaScript, stylesheets, images, and other artifacts of the packaging performed by the bundler.
 8. The system according to claim 1, wherein the cloud computing platform comprises: a platform compiler that is configured to: transform the source code of the package into application code of the cloud computing platform.
 9. The system according to claim 1, wherein the user interface components provide an eventing function for communication with the cloud computing platform that allows for integration with the application provided by the cloud computing platform.
 10. The system according to claim 1, wherein e user interface components provide an eventing integration function for interacting with resources from the external learning platform system within an iFrame, wherein the eventing integration function allows for the external learning platform to communicate with the application provided by the cloud computing platform.
 11. A method for integrating learning data provided by an external learning platform to create a custom learner experience within a context of an application provided by a cloud computing platform, the method comprising: exposing, via learner application programming interfaces (AP), a common learning data schema on the cloud computing platform; transforming, via a compiler of a user interface platform, source code of user interface components of a componentized learner user interface for usage on the cloud computing platform, wherein the user interface components are specific to the common learning data schema shared with the learner APIs; generating, via a bundler of the user interface platform, generate a package of user interface components that are compatible for usage on the cloud computing platform; the package from the user interface platform to the cloud computing platform and composing, at the cloud computing platform, the learning data provided via the learner APIs and the user interface components from the package to provide a custom learner experience within the context of the application provided by the cloud computing platform.
 12. The method according to claim 11, wherein the learner APIs and the user interface components have an implicit shared knowledge of the common learning data schema that results in interoperability between them.
 13. The method according to claim 11, wherein the cloud computing platform composes the learning data provided via the learner application interfaces and the user interface components from the package via at least one of: application code; composition in an experience builder by an administrator; or an automation application.
 14. The method according to claim 11, further comprising: implementing, at the cloud computing platform, external learning entities that are instances of the learner APIs that make the common learning data schema available within the cloud computing platform.
 15. The method according to claim 14, wherein the external learning entities comprise the learning data, and have attributes expected by the user interface components.
 16. The method according to claim 11, wherein the package of user interface components is specific to the cloud computing platform, and comprises: transformed source code, assets, configuration and metadata of the user interface components.
 17. The method according to claim 11, wherein the learning data comprises learning content and contextual user information, and wherein the package comprises: source code specifically compiled for interoperability with the target cloud computing platform, and includes one or more of: metadata, configuration, JavaScript, stylesheets, images, and other artifacts of the packaging performed by the bundler.
 18. The method according to claim 11, further comprising: transforming, at a platform compiler of the cloud computing platform, the source code of the package into application code of the cloud computing platform.
 19. The method according to claim 11, further comprising: providing, via the user interface components, an eventing integration function for interacting with resources from the external learning platform within an iFrame, wherein the eventing integration function allows for the external learning platform to communicate with the application provided by the cloud computing platform.
 20. A system for integrating learning data provided by an external learning platform to create a custom learner experience within a context of an application provided by a cloud computing platform, comprising: at least one hardware-based processor and memory, wherein the memory comprises processor-executable instructions encoded on a non-transient processor-readable media, wherein the processor-executable instructions, when executed by the processor, are configurable to cause: exposing, via learner application programming interfaces (APIs), a common learning data schema on the cloud computing platform; transforming, via a compiler of a user interface platform, source code of user interface components of a componentized learner user interface for usage on the cloud computing platform, wherein the user interface components are specific to the common learning data schema shared with the learner APIs; generating, via a bundler of the user interface platform, generate a package of user interface components that are compatible for usage on the cloud computing platform; exporting the package from the user interface platform to the cloud computing platform; and composing, at the cloud computing platform, the learning data provided via the learner APIs and the user interface components from the package to provide a custom learner experience within the context of the application provided by the cloud computing platform. 